,CottonGen has an instance of the JBrowse genome browser for viewing genome data. A list of the genomes available in CottonGen can be accessed by clicking the JBrowse link in the Tools menu.,Whole Genomes,Chloroplast Genomes,Please watch the JBrowse tutorial for more details about how to navigate and use JBrowse.,

,In 2017, cotton (Gossypium hirsutum L.) leafroll dwarf virus (CLRDV) was first reported in the United States. One CLRDV inoculum source includes the previous year’s cotton stalks, hence destroying cotton stalks could be effective for CLRDV management. However, tillage intensive stalk destruction methods (SDMs) can degrade southeastern soils, but a cover crop may provide short-term benefits and reduce CLRDV incidence. Therefore, we examined three SDMs (Tillage, Pull, Mow) across two cover crop levels [no cover and rye (Secale cereale L.) /clover (Trifolium incarnatum L.) mixture] and two cotton varieties to determine how cotton growth, soil penetration resistance (PR), and two CLRDV incidence sample times (pre-harvest and post-harvest) were affected across six environments during the 2021 and 2022 growing seasons. None of the SDMs affected any factors examined in this experiment, except soil PR and cotton yield. The Pull and Mow SDMs both increased soil PR compared to the Tillage SDM. An 8% yield increase (Pull > Mow) was observed, but the Tillage SDM yield did not differ from Pull or Mow SDMs. The rye/clover mixture also increased soil PR. Although cotton stands were 15% greater with no cover crop, subsequent cotton yield and fiber quality were minimally affected by cover crops. The rye/clover mixture increased post-harvest CLRDV incidence, and cotton yields were equal between cover crops. Pre-harvest CLRDV incidence probability was 0.23, but post-harvest CLRDV incidence probability was 0.71. Continuing to identify and evaluate cultural practices that reduce CLRDV incidence is imperative to prevent negative impacts.,This dataset contains all data and code required to reproduce the analyses, tables, and figures in the associated manuscript. A list of R packages used to create the aforementioned items can be found in the associated manuscript.,

Map of Cottontail distribution developed from a probability of occurrence map created using multi-scale vegetation, abiotic, and anthropogenic features.

,This dataset consists of growth and yield data for each season when upland cotton [Gossympium hirsutum (L.)] was grown for lint and seed at the USDA-ARS Conservation and Production Research Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). In the 2000 through 2004, 2008, 2010, 2012, and 2020 seasons, cotton was grown on from one to four large, precision weighing lysimeters, each in the center of a 4.44 ha square field also planted to cotton. The square fields were themselves arranged in a larger square with four fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field were thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Cotton was grown on different combinations of fields in different years. When irrigated, irrigation was by linear move sprinkler system years before 2014, and by both sprinkler and subsurface drip irrigation in 2020. Irrigation protocols described as full were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Irrigation protocols described as deficit typically involved irrigation at rates established as percentages of full irrigation ranging from 33% to 75% depending on the year.,The growth and yield data typically include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, boll mass (when present), lint mass, seed mass, final yield, and lint quality. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from only manual sampling on replicate plots in each field and lysimeters.,These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on cotton ET, crop coefficients, crop water productivity, and simulation modeling of crop water use, growth, and yield. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used for testing, and calibrating models of ET that use satellite and/or weather data.,See the README for descriptions of each data file.,

Probability map of Cottontail occurrence in relation to vegetation, abiotic, and anthropogenic features.

,This is digital research data corresponding to a published manuscript, Cover cropping history affects cotton boll distribution, lint yields, and fiber quality, in Crop Science, Vol. 63 p. 1209–1220.,There has been limited introduction of new cover crop species into cotton (Gossypium hirsutum L.) production within the last 30 years. Mounting evidence shows that traditional cover cropping species may be detrimental to cotton production, either by depleting soil fertility with crop removal, immobilizing minerals from high carbon residue, or excessive quantity of residue remaining at planting. The objective of this study was to determine the effects of growing a novel cover crop species, carinata (Brassica carinata A. Braun), as a winter annual cover crop for cotton rotation in the southeastern Coastal Plain. Over a 2-year period, carinata, winter wheat (Triticum aestivum L.), and fallow covers were maintained over winter months, then rotated into cotton. Each year, seedcotton and lint yields were collected, along with subsamples for ginning and subsequent fiber quality analyses. Additionally, end-of-season plant mapping was conducted on plants from 1-m of row per plot to determine cover crop effects on boll formation, retention, and distribution, as well as canopy architecture.,

,A high-quality reference genome is an essential tool for applied and basic research on arthropods. Long-read sequencing technologies may be used to generate more complete and contiguous genome assemblies than alternate technologies, however, long-read methods have historically had greater input DNA requirements and higher costs than next generation sequencing, which are barriers to their use on many samples. Here, we present a 2.3 Gb de novo genome assembly of a field-collected adult female Spotted Lanternfly (Lycorma delicatula) using a single PacBio SMRT Cell. The Spotted Lanternfly is an invasive species recently discovered in the northeastern United States, threatening to damage economically important crop plants in the region. The DNA from one individual female specimen collected in Reading, Berks County, Pennsylvania was used to make one standard, size-selected library with an average DNA fragment size of ~20 kb. The library was run on one Sequel II SMRT Cell 8M, generating a total of 132 Gb of long-read sequences, of which 82 Gb were from unique library molecules, representing approximately 38x coverage of the genome. The assembly had high contiguity (contig N50 length = 1.5 Mb), completeness, and sequence level accuracy as estimated by conserved gene set analysis (96.8% of conserved genes both complete and without frame shift errors). Further, it was possible to segregate more than half of the diploid genome into the two separate haplotypes. The assembly also recovered two microbial symbiont genomes known to be associated with L. delicatula, each microbial genome being assembled into a single contig. We demonstrate that field-collected arthropods can be used for the rapid generation of high-quality genome assemblies, an attractive approach for projects on emerging invasive species, disease vectors, or conservation efforts of endangered species.,Supporting files for the manuscript "A High-Quality Genome Assembly from a Single, Field-collected Spotted Lanternfly (Lycorma delicatula) using the PacBio Sequel II System", include several intermediate versions of the assembly (raw output from Falcon, raw output from Falcon unzip, etc.) as well as the final assembly primary contigs and haplotigs (for the regions of the genome that were phased).,,

국내에서 발생하는 고병원성 조류인플루엔자 바이러스를 분리하여 해당 바이러스의 HA 유전자의 염기서열이다. 해당 유전자 정보를 이용하여 다양한 연구에 활용이 가능할 것으로 판단한다.

● 데이터 키워드 - 유전체, 유전자, NGS, DNA ● 데이터 상품 요약 - 국내 식물 자생종과 동일한 종의 유전체 해독 원 데이터로부터 분석하여 얻어진 예측된 유전자 정보 ● 데이터 상품 정보 - 본 상품은 자생종 유전체 분석을 통해 얻어진 유전자의 유전자군 정보를 제공합니다. - 기능 도메인에 대해 기능별 유용성, 효소, 단백질, 병 저항성 유전자군 분류 가공 - 데이터 comparative analysis를 통해 유전자군별 발현 확률 및 계통 확률 계산 ● 컬럼 정보 - fasta format ● 활용 예제 - 본 데이터 상품을 활용하여 사용자는 다음과 같은 정보를 확인할 수 있습니다. 1) 신약 및 기능성 식품, 화장품 개발 관련 분야 기초자료 ● 데이터 상품 정보 - 2019년 7월 ~ 2019년 12월 [원본 데이터](https://www.bigdata-forest.kr/product/GNM100701)는 로그인 후 구매하여 다운로드 하십시오.

,This is USDA-ARS data from the publication: "Performance of a cotton gin machine that removes plastic contamination from seed cotton." The study was conducted during the Fall of 2020 with continued data analysis thru September 2021. Plastic contamination is the most pressing issue for the U.S. cotton industry today. Due to this problem, U.S. cotton has lost the 7 cents per pound premium it brought on international cotton markets. Plastic contaminants are often introduced prior to ginning, but cotton gin machinery can tear and shred the plastics so that they become more difficult to remove. A cotton gin machine developed and used to combat the plastic contamination problem in Chinese cotton was tested to compare its performance at removing typical plastics found in U.S. cotton to that of conventional cotton gin seed cotton cleaners.,